Statistical methods to detect novel genetic variants using publicly available GWAS summary data

Bin Guo, Baolin Wu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We propose statistical methods to detect novel genetic variants using only genome-wide association studies (GWAS) summary data without access to raw genotype and phenotype data. With more and more summary data being posted for public access in the post GWAS era, the proposed methods are practically very useful to identify additional interesting genetic variants and shed lights on the underlying disease mechanism. We illustrate the utility of our proposed methods with application to GWAS meta-analysis results of fasting glucose from the international MAGIC consortium. We found several novel genome-wide significant loci that are worth further study.

Original languageEnglish (US)
Pages (from-to)76-79
Number of pages4
JournalComputational Biology and Chemistry
Volume74
DOIs
StatePublished - Jun 2018

Bibliographical note

Funding Information:
This research was supported in part by NIH grant GM083345 and CA134848 . We want to thank the editor and reviewers for their constructive comments that have greatly improved the presentation of the paper. We are grateful to the University of Minnesota Supercomputing Institute for assistance with the computations. Data on glycemic traits have been contributed by MAGIC investigators and have been downloaded from http://www.magicinvestigators.org . We want to thank Dr. James Pankow for pointing out the MAGIC data source to us.

Publisher Copyright:
© 2018 Elsevier Ltd

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • GWAS
  • SNP-set association test
  • Summary statistics

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